RNN: Recurrent Neural Networks were designed to allow neural networks to have temporal information, which simple neural networks cannot have [1]}. Basically, RNNs form a chain structure in which each node receives as input the output from the predecessor node and one part of the input sequence (e.g., a word or a vector). Each node outputs a value, both to the successor node and to the next layer.
